Vancomycin-Modified Nanoparticles for Efficient Targeting and Preconcentration of Gram-Positive and Gram-Negative Bacteria
Bibliographic record
Abstract
A series of vancomycin-modified nanoparticles were developed and employed in magnetic confinement assays to isolate a variety of Gram-positive and Gram-negative bacteria from aqueous solution. We determined that the orientation/architecture of vancomycin on the surface of the nanoparticles and the overall surface coverage is critical in mediating fast and effective interactions between the nanoparticle and the pathogen cell wall surface and only one orientation/architecture in a series of modified nanoparticles leads to the efficient and reproducible capture of several important pathogenic bacteria. Interestingly, as the nanoparticles increase in diameter (from approximately 50 to 2800 nm), it is necessary to incorporate a long linker between the nanoparticle surface and the vancomycin moiety in order for the surface bound probe to efficiently confine Gram-positive bacteria. Finally, we also determined that the time required for efficient labeling and subsequent magnetic confinement significantly decreases as the size of the nanoparticle and the vancomycin surface coverage on the nanoparticle increases. As disease detection technologies transition to "lab-on-a-chip" based platforms it is necessary to develop strategies to effectively and inexpensively preconcentrate cells from large volume to volumes more amenable to these types of microfluidic devices. These small molecule-modified superparamagnetic nanoparticles can provide a means by which this can be accomplished.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".